@naught101 I think I probably meant scipy, not sure if it's related... looks like pip is too late in the python path. I strongly recommend using anaconda/conda, it's vastly superior to pip IMO.
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haydMar 12 at 2:47

In addition to Novarchibald's addition, it is generally a good idea to create a virtual environment for your python project and install dependencies inside. This allows you to have better control over dependencies and their versions. To set up a virtual environment, enter:

virtualenv --no-site-packages --distribute my_python_project

Then, activate it with:

cd my_python_project
source bin/activate

At this point anything that you install with pip will be kept inside this virtual environment. If you want to install something globally, then you should first exit virtualenv with:

Alternatively: using the pip from the virtualenv bin directory (here my_python_project/bin/pip) you maintain that environment, without a need to "activate" it. Then - using any of scripts installed in the bin is using given virutalenv. "activate" is only handy to make calls to "python", "easy_install" and "pip" using virtualenv specific bin.
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Jan VlcinskyJun 17 '13 at 11:54

For global Python packages, use either the Ubuntu Software Center, apt-get or synaptic

Ubuntu uses Python for many important functions, therefore interfering with Python can corrupt your OS. This is the main reason I never using pip on my Ubuntu system, but instead I use either Ubuntu Software Center, synaptic or apt-get, which all by default install packages from the Ubuntu repository. These packages are tested, usually pre-compiled so they install faster and ultimately designed for Ubuntu. In addition all required dependencies are also installed and a log of installs is maintained so they can be rolled back. I think most packages have corresponding Launchpad repos so you can file issues.

Another reason to use either Ubuntu packages is that sometimes these Python packages have different names depending on where you downloaded them from. Python-chardet is an example of a package which at one time was named one thing on PyPI and another thing in the Ubuntu repository. Therefore doing something like pip install requests will not realize that chardet is already installed in your system because the Ubuntu version has a different name, and consequently install a new version which will corrupt your system in a minor insignificant way but still why would you do that.

In general you only want to install trusted code into your OS. So you should be nervous about typing $ sudo pip <anything>.

Lastly some things are just easier to install using either Ubuntu packages. For example if you try pip install numpy to install numpy & scipy unless you have already installed gfortran, atlas-dev, blas-dev and lapack-dev, you will see an endless stream of compile errors. However, installing numpy & scipy through the Ubuntu repository is as easy as...

$ sudo apt-get install python-numpy python-scipy

You are in luck, because you are using Ubuntu, one of the most widely supported and oft updated distributions existing. Most likely every Python package you will need is in the Ubuntu repository, and probably already installed on your machine. And every 6 months, a new cycle of packages will be released with the latest distribution of Ubuntu.

If you are 100% confident that the package will not interfere with your Ubuntu system in any way, then you can install it using pip and Ubuntu is nice enough to keep these packages separate from the distro packages by placing the distro packages in a folder called dist-packages/. Ubuntu repository has both pip, virtualenv and setuptools. However, I second Wojciech's suggestion to use virtualenv.

For personal Python projects use pip and wheel in a virtualenv

If you need the latest version, or the module is not in the Ubuntu repository then start a virtualenv and use pip to install the package. Although pip and setuptools have merged, IMO pip is preferred over easy-install or distutils, because it will always wait until the package is completely downloaded and built before it copies it into your file system, and it makes upgrading or uninstalling a breeze. In a lot of ways it is similar to apt-get, in that it generally handles dependencies well. However you will have to handle some dependencies yourself, for example as mentioned above for NumPy and SciPy make sure you have installed gfortran, atlas-dev, blas-dev and lapack-dev from the Ubuntu repository.

Since you may end up installing these many times, consider using wheel with pip to create a wheelhouse

~(my_py_proj)$ pip wheel numpy scipy

This will create binary wheel files in <cwd>/wheelhouse, use -d to specify a different directory. Now if you start another virtualenv and you need the same packages you've already built them and you can install them form your wheelhouse using pip install --find-links=<fullpath>/wheelhouse